Machine Learning Foundations: A Case Study Approach

This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.

( 978 Reviews )

Created by: Carlos Guestrin

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Overall Score : 96 / 100

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Course Description

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems?In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains.This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications.Learning Outcomes: By the end of this course, you will be able to:-Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering.-Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.-Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task.-Utilize a dataset to fit a model to analyze new data.-Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python.

Time: 24h

Instructor Details

Carlos Guestrin

4.8 Rating
978 Reviews

Carlos Guestrin is the Amazon Professor of Machine Learning at the Computer Science & Engineering Department of the University of Washington. He is also a co-founder and CEO of Dato, Inc., focusing on making it easy to build intelligent applications that use large-scale machine learning at their core. His previous positions include the Finmeccanica Associate Professor at Carnegie Mellon University and senior researcher at the Intel Research Lab in Berkeley. Carlos is a recipient of a National Science Foundation CAREER Award, an Alfred P. Sloan Fellowship, and the Stanford Centennial Teaching Assistant Award. Carlos was also named one of the 2008 `Brilliant 10' by Popular Science Magazine, received the IJCAI Computers and Thought Award from the top AI conference, and the Presidential Early Career Award for Scientists and Engineers (PECASE) from President Obama.

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